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Daily regression parameters k and d can be taken from Figures 7 and 8 to compute ρsim using the HS data of a certain day as input. As a first model test, all HS records of the station data are used to simulate ρsim (Equation (4)). Values of the regional offset parameter OSreg are shown in Table 3. OSreg is highest in regions where deep snow covers are commonly observed (e.g., region 1). In both elevation zones, absolute errors (AE) of SWE are lower than absolute errors of ρ since this approach benefits from observed HS as input (Figure 9). The curves of the relative mean absolute errors (MAErel) of SWE are also calculated using the ±16 day window. MAErel is constantly higher in the lower elevation zone and both error curves decrease over the course of the season (except April/May <1,400 m a.s.l.).
Table 3

Regional offset parameters [kg m−3] of Equation (4) (locations of the snow regions are given in Figure 1)

Snow region< 1,400 m> 1,400 m
– 34.9 
7.3 – 
−5.7 −13 
−12.3 34.3 
1.4 −18.1 
Snow region< 1,400 m> 1,400 m
– 34.9 
7.3 – 
−5.7 −13 
−12.3 34.3 
1.4 −18.1 
Figure 9

Boxplots giving the distribution of absolute errors of ρ and SWE of the Tyrolean station data (boxes represent the 25 to 75% interquartile range with the line in the middle indicating the median; whiskers represent 1.5 times interquartile ranges added to the 25 and 75% percentiles). Curves of relative errors of SWE (calculated with ±16 day moving windows) show the increasing model accuracy with the advancing season.

Figure 9

Boxplots giving the distribution of absolute errors of ρ and SWE of the Tyrolean station data (boxes represent the 25 to 75% interquartile range with the line in the middle indicating the median; whiskers represent 1.5 times interquartile ranges added to the 25 and 75% percentiles). Curves of relative errors of SWE (calculated with ±16 day moving windows) show the increasing model accuracy with the advancing season.

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